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Title Birth Cohort Consortium of Asia : Current and Future Perspectives

Author(s)Kishi, Reiko; Zhang, Jun Jim; Ha, Eun-Hee; Chen, Pau-Chung; Tian, Ying; Xia, Yankai; Tsuchiya, Kenji J.; Nakai,Kunihiko; Kim, Sungkyoon; Hong, Soo-Jong; Hong, Yun-Chul; Lee, Jeong-Rim; Hamid Jan B. Jan Mohamed; Parajuli,Rajendra Prasad; Adair, Linda S.; Chong, Yap Seng; Guo, Yue Leon; Wang, Shu-Li; Nishijo, Muneko; Kido, Teruhiko;Tai, Pham The; Nandasena, Sumal

Citation Epidemiology, 28(Supplement 1), S19-S34https://doi.org/10.1097/EDE.0000000000000698

Issue Date 2017-10

Doc URL http://hdl.handle.net/2115/71564

Rights This is a non-final version of an article published in final form in Epidemiology : Oct-2017, 28(Supplement 1),pp.S19‒S34.

Type article (author version)

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File Information Epidemiology28_S19.pdf

Hokkaido University Collection of Scholarly and Academic Papers : HUSCAP

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Type of manuscript: original article

Birth Cohort Consortium of Asia (BiCCA) - Current and Future Perspectives

Reiko Kishi,a Jun Jim Zhang,b Eun-Hee Ha,c* Pau-Chung Chen,d,e* Ying Tian,b,f Yankai Xia,g

Kenji J Tsuchiya,h Kunihiko Nakai,i Sungkyoon Kim,j Soo-Jong Hong,k Yun-Chul Hong,l

Jeong-Rim Lee,m Hamid Jan B Jan Mohamed,n Rajendra Prasad Parajuli,o Linda S Adair,p

Yap Seng Chong,q Yue Leon Guo,d,e,r Shu-Li Wang,r Muneko Nishijo,s Teruhiko Kido,t Pham

The Tai,u Sumal Nandasena,v

aCenter for Environmental and Health Sciences, Hokkaido University, Sapporo, Japan;

bMOE and Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital,

Shanghai Jiao Tong University School of Medicine, Shanghai, China;

cDepartment of Preventive Medicine, Ewha Medical Research Center, Ewha Womans

University, Seoul, South Korea;

dInstitute of Occupational Medicine and Industrial Hygiene, National Taiwan University

College of Public Health, Taipei, Taiwan;

eDepartment of Environmental and Occupational Medicine, National Taiwan University

Hospital and National Taiwan University College of Medicine, Taipei, Taiwan;

fDepartment of Environmental Health, School of Public Health, Shanghai Jiao Tong

University School of Medicine, Shanghai, China;

gState Key Lab of Reproductive Medicine, School of Public Health, Nanjing Medical

University, China;

hHamamatsu University School of Medicine, Research Center for Child Mental Development,

Japan;

iTohoku University Graduate School of Medicine, Japan;

jGraduate School of Public Health, Seoul National University, South Korea;

kDepartment of Pediatrics, Childhood Asthma and Atopy Center, Environmental Health

Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South

Korea; lEnvironmental Health Center, Seoul National University, South Korea;

mThe Korean Institute of Child Care and Education (KICCE), South Korea;

nNutrition Program, School of Health Sciences, Universiti Sains Malaysia,

Malaysia; oUniversity of Tokyo, Dept of Human Ecology, Japan;

pCarolina Population Center, University of North Carolina at Chapel Hill, USA;

qDepartment of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National

University of Singapore, Singapore;

rNational Institute of Environmental Health Sciences, National Health Research Institutes,

Taiwan;

sDepartment of epidemiology and public health, Kanazawa Medical University, Uchinada,

Ishikawa, Japan;

tInstitute of Medical, Pharmaceutical and Health Sciences, Faculty of Health Sciences,

Kanazawa University, Kanazawa, Japan;

uBiomedical and pharmaceutical research center, Vietnam Military Medical University, Ha

Noi, Vietnam;

vNational Institute of Health Sciences, Kalutara, Sri Lanka;

*Co-corresponding.

Corresponding author:

Eun-Hee Ha MD PhD

Professor

Department of Preventive Medicine

Ewha Womans University College of Medicine

911-1, Mok-6 dong, Yangcheonku, Seoul, 158-056, Korea

Phone: +82-2-2650-5757

Fax: +82-2-2653-1086

E-mail: [email protected]

Pau-Chung Chen MD PhD

Professor and Director

Institute of Occupational Medicine and Industrial Hygiene

National Taiwan University College of Public Health

17 Xuzhou Road, Taipei 10055, Taiwan

Phone: +886-(0)2-3366 8088

Fax: +886-(0)2-3366 8734

Email: [email protected]

Running Title: Birth cohort studies in Asia

Key Words: birth cohort, children’s environmental health, birth cohort consortium of Asia

(BiCCA)

Conflicts of Interest and Source of Funding: The authors have declared that no competing

interests exist. The PI meetings of this consortium were supported financially by grants from

National Science Council (NSC-101-2911-I-002-009) and National Taiwan University

Hospital (NTUH-103-A123, NTUH-104-A123), Taiwan.

Acknowledgments: We thank BiCCA secretariat Dr. Mei Huei Chen to organize the tables

and content of this manuscript. We also thank all cohort researchers and staffs who have

helped with the completion of the inventories.

http://tw.f178.mail.yahoo.com/ym/[email protected]

ABSTRACT

Background: The environmental health of children is one of the great global health

concerns. What a developing child is exposed to in utero and in his/her early years has

major consequences on later health. However, environmental risks or disease burdens

vary from region to region. Birth cohort studies are ideal for investigating different

environmental risks.

Methods: The principal investigators of three birth cohorts in Asia including the

Taiwan Birth Panel Study (TBPS), the Mothers and Children's Environmental Health

Study (MOCEH), and the Hokkaido Study on Environment and Children' Health

(Hokkaido Study) co-established the Birth Cohort Consortium of Asia (BiCCA) in

2011. Through a series of five PI meetings, the enrolment criteria, aim of the

consortium, and a first-phase inventory were confirmed.

Results: To date, 23 birth cohorts have been established in 10 Asian countries,

consisting of approximately 70,000 study subjects in the BiCCA. This article provides

the study framework, environmental exposure and health outcome assessments, as

well as maternal and infant characteristics of the participating cohorts.

Conclusions: The BiCCA provide a unique and reliable source of birth cohort

information in Asian countries. Further scientific cooperation is ongoing to identify

specific regional environmental threats and improve the health of children in Asia.

1

INTRODUCTION

Asia contains half of the world's children, and the countries of Asia are the most

rapidly industrializing nations in the world. Environmental threats to the health of

children are diverse in Asia and include classic infectious disease hazards (i.e.,

pneumonia, dysentery, measles, Acquired Immune Deficiency Syndrome, and tu-

berculosis). Additionally, the prevalence of environmentally related diseases such as

allergic disease, attention deficit hyperactivity disorder (ADHD), and autism is in-

creasing dramatically.1,2 As industrial development proceeds and as nations in the

region pass through the epidemiologic transition, rapid urbanization, unsustainable

consumption, and an increase of industrial disposal such as e-waste, children are

confronted by a rapidly multiplying array of new health threats posed by exposures

to toxic chemicals.1

To address these problems, local and national research efforts must focus on envi-

ronmental risks. In 2002, the International Conference on Environmental Threats to

the Health of Children was held in Bangkok to initiate the very first step towards

awareness about the environmental health hazards affecting children in these regions.

Several actions have been proposed to decrease environmental hazards, including

removal of lead from gasoline, the India clean-water project, the mercury pollution

2

reduction project, international networking and anti-smoking campaigns.3 Neverthe-

less, new challenges continue to emerge. In 2008, hundreds of thousands of infants

and young children suffered from renal dysfunction after exposure to Melamine that

was deliberately added to milk and milk-containing products in China, Hong Kong

and Taiwan.4,5 Later in 2011, plasticizers such as di(2-ethylhexyl) phthalate (DEHP)

and di-iso-nonyl phthalate (DiNP) were found to be illegally added to clouding

agents used in foods and beverages in Taiwan.6 These issues draw new attention to

the importance of food safety. Another trans-boundary threat in the Asia-Pacific re-

gion is outdoor air pollution. Smog and sandstorm in China and Southeast Asia haze

are not regional threats. Even more, we are facing the challenge of climate change

and extreme weather globally. This issue has become the prominent target of policy

reforms and public health efforts.

In response to these new threats, national governments in Asia have taken action

since the 1970s to protect children against environmental threats to health, and in the

past decade, they have developed new approaches to evaluating and managing toxic

chemicals. In 2009, the Ministry of Environment in Korea held the Third Interna-

tional Conference for Children's Health and Environment in Busan, Korea in part-

nership with the World Health Organization.7 In July 2010, in Jeju, Korea, the Gov-

3

ernment of Korea hosted the Second Ministerial Regional Forum on Environment

and Health in South-East and East Asian Countries, which was jointly organized by

the United Nations Environment Program Regional Office for Asia and the Pacific

and the World Health Organization Regional offices for the Western Pacific and

South-East Asia.8 Some associated conferences have further pledged to advocate for

the recognition, assessment and consideration of hazardous environmental influ-

ences on children’s health and development.

Children’s environmental health is an issue of both global and regional importance.

Environmental hazards, lifestyle and genetic susceptibility vary across different re-

gions and races. Thus, a single one cohort or study is unable to explore the whole

picture of this issue. Population size is even the threshold of study investigating

gene- environmental interactions or co-exposure effects. In addition, replication or

validation is crucial for interpretation of results from omic technologies such as

epigenomics and metabolomics. Harmonization of cohort studies has been successes

by ENRIECO (Environmental Health Risks in European Birth Cohorts) 9 and pro-

jects in Europe 10, but not elsewhere. A useful network to exchange information will

facilitate international cooperation for exploring regional and global hazards, under-

standing the health impact of environmental toxicants, and developing effective

4

prevention strategies not only in Asia but also across the world.

5

METHODS

Establishment of the Birth Cohort Consortium of Asia (BiCCA)

The Birth Cohort Consortium of Asia was co-established in 2011 by the principal in-

vestigators (PIs) of the following three birth cohorts in Asia: the Taiwan Birth Panel

Study (TBPS) from Taiwan (PI: Chen), the Mothers and Children's Environmental

Health Study (MOCEH) from Korea (PI: Ha), and the Hokkaido Study on Environ-

ment and Children' Health (Hokkaido Study) from Japan (PI: Kishi). The two main

objectives of BiCCA are 1. to facilitate the exchange of knowledge and collaboration

between cohorts and researchers, and 2. to explore the future need for children’s en-

vironmental health research. The agenda of the BiCCA was first introduced at the

PPTOXIII (Prenatal Programming and Toxicity III) held on May 14-15, 2012 in Paris,

France. Later on, several symposia/seminar, workshop and PI meetings were held to

increase the knowledge about the Asian birth cohorts and to build capacity related to

conducting exposure measurements, and the latest techniques such as genetics and

epigenetics analysis. These activities also determine the criteria to join BiCCA, com-

plete the 1st phase inventory, prepare the introduction paper and plan for future direc-

tion of collaboration. The details are listed as eTable 1.

Definition and identification of Asian birth cohorts

6

Birth cohort studies were included in the project if they 1) were conducted in Asia; 2)

collected information about both prenatal and postnatal exposures; 3) aimed to in-

vestigate environmental and health issues in children; and 4) enrolled more than 200

participants. The last criteria is a guideline but not an absolute exclusion criteria.

Eligible cohorts were identified through a variety of sources, as follows: searchable

websites, publications and personal contacts.

1st phase inventory

An inventory questionnaire was designed to collect detailed information from the

various cohorts about their study design, biological sample collection, measurement

of environmental exposure, and assessment of child health outcomes. The full ques-

tionnaire is listed as Online Supplemental Material. The questionnaire was divided

into the following 3 sections:

1) Basic information: name of the birth cohort, information about the PI and con-

tact person

2) Basic description: main aim, number of children/mother/father recruited, date

and criteria of enrolment, and planned age of children at the end of the fol-

low-up.

3) Questionnaire or registry date and biological samples: birth outcomes of the

7

children, the children’s exposure, the children’s growth and development, social

factors, diet and nutrition, children’s biological samples and outcome assess-

ments; maternal characteristics, maternal exposure and biological samples; en-

vironmental exposure measurement including air pollution, phenol, heavy metals,

pesticide, nutrients, persistent organic pollutants, plasticizers, flame retardants,

tobacco smoke; genetic and epigenetic factors; outcome assessments including

birth outcomes, malformation, allergic diseases, behaviours, neurodevelopment,

sex differentiation and reproductive function.

8

RESULTS

The BiCCA includes 23 birth cohorts, totalling approximately 70,000 study subjects,

that were conducted in 10 Asian countries (Figure 1), including three in China, four

in Japan, five in Korea, one in Malaysia, one in Nepal, one in the Philippines, one in

Singapore, one in Sri Lanka, three in Taiwan and three in Vietnam. Table 1 lists the

full name of birth cohorts as well as the enrolment period and the numbers of par-

ticipants.11-24 The CLHNS is the oldest cohort that began in 1983, while the Nanjing

Medical University Birth Cohort (NJMUBC) and the Kalutara Children’s Health

Study (KCHS) are the newest two, beginning in 2014. The majority of the cohorts

enrolled at least 200 participants, with the current largest cohort (Hokkaido Study)

having 20,818 participants. Two exceptions were the Universiti Sains Malaysia

Pregnancy Cohort Study (USM Pregnancy Cohort), which had enrolled 159 children

and 188 mothers, and the Nepali Birth Cohort Study in Chitwan Valley (Nepali)

from Nepal with 100 participants.

Figure 2 shows the different time points of enrolment and follow-up in the birth co-

horts collaborating in the BiCCA. The predefined periods included pre-pregnancy,

three trimesters of pregnancy, and newborn to the adolescent stage of participating

children. Most of the cohorts recruited mothers during pregnancy (n=15) or before

9

pregnancy (n=2), and the remaining began at birth. Participating children were fol-

lowed or were planned to be followed at least two points before the age of 2 years

and then once every 2 to 5 years until puberty. The time points already performed

and those planned are marked with different symbols. At our reference date (March

2015), children in the 10 cohorts were between 5 to 10 years of age, children in 6

cohorts were older than 10 years, and in seven cohorts, they were younger than 5

years of age (Figure 2).

Biological samples were collected in all cohorts with two exception [Panel Study on

Korean Children (PSKC) and KCHS] from mothers and children, covering the pre-

natal and postnatal periods with various types of tissues including amniotic fluid,

blood, breast milk, cord blood, deoxyribonucleic acid (DNA), hair, meconium, pla-

centa, stool, nail, urine and umbilical cord collected (Table 2). The NJMUBC, SBC

and The COhort for Childhood Origin of Asthma and allergic diseases (COCOA)

collected blood and DNA from biological fathers. Urine and semen were collected

additionally by the former two cohorts.

Heavy metals were the most commonly measured environmental exposure, but some

cohorts (especially those conducted in Japan and Korea) only focused on mercury,

10

cadmium or lead. About half of the participating cohorts collected information about

tobacco smoking by measuring biomarkers. COCOA, MOCEH and KCHS meas-

ured information about both indoor and outdoor air pollution. The Hokkaido Study

from Japan collected indoor dust to measure mite allergens and phthalate, among

others. The KCHS focused mainly on exposure to PM2.5 and Black carbon. Around

half of the cohorts measured emerging environmental pollutants, such as persistent

organic pollutants (dioxin and related compounds, polyfluoroalkyl substances or

brominated flame retardants), bisphenol A and phthalate. The cohorts in Vietnam

focused mainly on dioxin exposure (Table 3).

All of the studies had information about birth outcomes, including birth weight and

gestational age, and most of them collected the body weight and height of children

at a later follow-up visit. The GUSTO from Singapore performed comprehensive

measurements of body composition of children including skinfolds and magnetic

resonance imaging (MRI). Congenital malformations were recorded in about half of

participating cohorts. Twelve studies assessed allergic diseases using The Interna-

tional Study of Asthma and Allergies in Childhood questionnaires (ISAAC). Neuro-

development and behaviour problems were the most important outcomes for most of

the studies, but the studies used a diverse set of measurement tools. Most studies

11

measured endocrine function and puberty status (or plan to do so in the future) (Ta-

ble 4).

Maternal characteristics are shown in Table 5-1 and 5-2. The average maternal age

and standard deviation among the cohorts ranged from 22.9 ± 3.7 (Nepali) to 33.5 ±

4.1 years (Children’s Health and Environmental Chemicals in Korea, CHECK). The

maternal pre-pregnancy BMI did not demonstrate large variations, ranging from

20.7 ± 2.6 (COCOA) to 23.7 ± 4.8 kg/m2 (GUSTO). Comparisons of maternal edu-

cational levels among participants of the different cohorts are a little complex, as

shown in Table 5-1 based on the education system of each country. The CLHNS had

the highest percentages of multipara (76.9%), and the Laizhou Wan Birth Cohort

(LWBC) in China had the highest percentage of primipara (67.3%) among cohorts

(Table 5-2). The mothers in the Sapporo birth cohort (Japan) had the highest per-

centage of smokers (20.3%) and alcohol drinkers (30.9%) during pregnancy (Table

5-2).

The infants’ gender ratio was diverse among the cohorts (Table 6); the percentage of

males ranged between 45.1% (USM Pregnancy Cohort) and 57.4% (DaNang Dioxin

Cohort Study, DaNang study). Although the average gestational ages were almost

12

the same, but the range of birth weights among the infants was wide; the infants’

birth weight in the KCHS (2940 ± 567 g) was the lowest, and LWBC (3419 ± 500 g)

had the highest infant birth weights among the cohorts. This variation may be due to

differences in the recruitment and source of populations among the cohorts. The

CLHNS study had the highest percentage of infants with preterm birth (15.6%), and

low birth weight (14.1%).

DISCUSSION

The BiCCA enrolled cohorts established in Asia that were diverse in terms of indus-

trialization and urbanization. The regional and/or global differences need to be dis-

covered through an integrated platform; for example, we are able to explore the dif-

ferences in cohort findings not only between developed versus developing Asian

countries but also with European or North America cohorts. Information including

standardized or state-of-art analytic methods, cohort design, assessments of health

outcome can be smoothly exchanged. In addition, the experiences shared between

studies or countries will help to promote environmental child health through health

policy-making or strategy implementation. Furthermore, the knowledge derived

from the BiCCA should note the future need for research of new emerging environ-

mental threats or public health priorities that contribute towards organizing the next

13

generation of birth cohorts.

The theory “Developmental Origins of Health and Disease, DOHaD” illustrated that

exogenous maternal malnutrition or environmental factors during pregnancy cause a

lifelong, persisting adaptation of the foetus resulting in low birth weight, in-

creased cardiovascular risk, and non-insulin dependent diabetes as adults.25 However,

the science underlying the impact of environmental factors on children’s health

(from the stage of the foetus to the age of 18) is more complex. While most of the

cohort studies in the BiCCA focused on environmental pollutants, CLHNS, GUSTO,

USM Pregnancy Cohort and Nepali were designed to investigate the role of nutrients.

Integration of participating cohorts in BiCCA with different study focus such as nu-

trition, social economic factors and environmental toxicants are promising to testify

the DOHaD hypothesis and related mechanism.

From the aspect of the exposure assessment, many East Asian countries have some

unique features compared with European countries and the USA. For example, the

Perfluoroalkyl substances (PFASs) are persistent organic pollutants that are detected

in humans worldwide. Their concentrations in plasma in Japan and Korea are quite a

lot lower than in western countries, and the temporal trends of PFAS levels indicate

14

that PFOS (perfluorooctyl sulfonate) and PFOA (perfluorooctanoic acid) concentra-

tions are decreasing every year due to the restriction of PFOS by the Stockholm

convention on POPs in 2009. However, the levels of long-chain PFASs (i.e., PFNA,

PFUnDA, and PFTrDA) seem to be increasing in Japan and Korea because they are

manufactured primarily in Japan via oxidating a mixture of linear fluorotelomer ole-

fins, and prenatal exposure to these compounds may have adverse health out-

comes.26, 27 In Taiwan, the concentrations of PFOS and PFOA in rivers near semi-

conductor and electronics industries are higher than in other countries, and industrial

wastewater treatment plants are a source of contamination.28 Therefore, the related

environmental hazards of PFASs could be an issue among countries with flourishing

high-tech or electronics industries. In contrast, the use of polybrominated diphenyl

ethers (PBDEs) in Japan diminished in the early 1990s following the recommenda-

tions of the Japanese Flame Retardants Conference for voluntary controls, and

phosphate esters (PFRs) have been the most frequently used organic flame retardants.

In 2003, the EU banned the use of PBDEs, and only a few epidemiological studies

on the alternatives to PBDEs have been reported. Therefore, Asian cohort studies

provide information about PFRs in indoor environments and their relationship to

asthma and allergies in children.29 The use of other alternative flame retardants, e.g.,

non-brominated flame retardants, is increasing and warrants further studies as well.

15

Different eating habit or culture may result in diverse chemical exposures or health

impact. For example, the benefits of essential fatty acid and toxicity of chemical

pollutants, such as mercury and dioxin related compounds, are always the scientific

basis of fish consumption advisories. Maternal plasma fatty acid concentrations

were significantly correlated with PFOS concentrations and may affected birth

weight of female offsprings.30 Associations between prenatal methylmercury expo-

sure and cognitive deficits were reported in a Faroe Islands cohort, but few were

found by the Seychelles Islands’ study. Evidence of negative confounding by prena-

tal docosahexaenoic acid (DHA) which partially obscured the association of prenatal

mercury with lower Intelligence quotient had reported. 31 Meanwhile, the adverse

health impacts of mercury were only observed among those children carrying the

susceptible APOE (Apolipoprotein E) genotype in TBPS.32 Genetic variation be-

tween races or ethnicities could further impose a diverse susceptibility to environ-

mental pollutants. In addition, canned food is common in Western countries whereas

hot food in plastic containers is common in Asia. Whether these habits increase the

exposure risk to certain endocrine disrupters such as bisphenol A or phthalate or not

is another question to be solved. The complex interaction between diet or nutrients,

co-exposure to environmental pollutants and genetic polymorphisms are crucial is-

16

sues in children’s health.

Climate change and extreme weather are potential environmental threats. Season

and weather especially cold ambient temperature have been identified as potential

risk factors for lower birth weight and preterm birth.33, 34 Nevertheless, null associa-

tion had been reported.35 Meanwhile, evidence is growing that climate change has

affect human health including mortality from extreme weather events, changes in

quality of air and water and changes in the ecology of infectious disease. Humidity

and temperature may change the composition of ambient air pollution and further

cause diverse toxic effects. The survival and spread of infectious pathogens are

highly correlated with weather. For example, the Aedes species mosquito carrying

Zika virus is distributed in tropical and subtropical regions that Taiwan and the

southern Chinese island province of Hainan could be at greater risk of locally trans-

mitted cases. However, the effect of global warming is unclear. Climate change is a

global issue and its related knowledge should be uncovered through international

cooperation.

The BiCCA consortium intends to expand beyond Asia and provide collaborations

among cohorts. One of the major challenges is the comparison of existing data

17

among the studies and harmonization of the exposure assessment and children’s

health outcome. Definition of basic demographic variables and questionnaires

should be harmonized. The potential bias of environmental exposure measurements

derived from various analytic methods or specimen could be verified by inter- and

intra- laboratory tests. Homogeneity of country or language specific assessment

tools needs to be identified. In addition, two different approach including pooled

analysis and meta-analysis will be applied. 36 Reviewing existing data will provide a

potential or direction for future collaborative analyses. Furthermore, evaluating new

methods and technologies for future studies will be enhanced by aggregation of data

from each cohort. After overcoming these challenges, combining and harmonizing

cohort data in this consortium will help to improve statistical power, reduce publica-

tion bias, and assess the exposure-outcome relationship in emerging concerns of

child health, even for relatively rare health outcomes. Further contribution will be

made for researches of co-exposure effects, gene-environmental interaction and rep-

lication and validation in omic studies through transnational and interracial coopera-

tion.

The BiCCA especially welcomes birth cohorts in Asia to collect epidemiological

data across different Asian countries. The BiCCA provides detailed information

18

about the Asian birth cohorts and facilitates access to information regarding existing

Asian cohort studies. Any group can contact us for further information or consider

potential collaboration from all around the world. Enquiries and initial approaches

regarding membership or detailed information are welcome. Please contact us

at [email protected]; decisions will be made by the Executive Committee Profes-

sors Reiko Kish, Jun Jim Zhang, Eun-Hee Ha and Pau-Chung Chen. More infor-

mation is available in our website (http://www.bicca.org/).

mailto:[email protected]

http://www.bicca.org/

19

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25

FIGURE LEGENDS

Figure 1. Asian birth cohort studies collaborating in BiCCA.

26

Figure 2. Start of enrollment and time points of follow up participating studies in

BiCCA. See Table 1 for full names and locations of cohorts.

Fig. 1

Fig. 2

Table 1. General description of participating studies in Birth Cohort Consortium of Asia

Country Birth cohort Full name and key reference Regions covered Source Enrollment period

No. of children at birth

No. of mother enrolled

CHINA LWBC Laizhou Wan Birth Cohort

South coast area of Laizhou Wan of Bohai Sea, Shandong province, China

Hospital based 2010-2013 773 773

NJMUBC

Nanjing Medical University Birth Cohort

Nanjing, Suzhou, Wuxi, Huai'an, Changzhou

Hospital & Community based

2014-2016 26,000* 30,000*

SBC Shanghai Birth Cohort Shanghai Hospital based 2013-2015 3,000 4,000

JAPAN HBC Study Hamamatsu Birth Cohort for Mothers and Children11

Hamamatsu Hospital & Community based

2007-2011 1,258 1,138

Hokkaido

Hokkaido cohort: Hokkaido Study on Environment and Children's Health12,13

Hokkaido Hospital & Community based

2003-2013 20,818 20,929

Sapporo

Sapporo cohort: Hokkaido Study on Environment and Children's Health12,13

Sapporo Hospital based 2002-2005 504 514

TSCD

The Tohoku Study of Child Development14

Sendai Hospital based 2001-2006 1,348 1,323




KOREA CHECK Children’s Health and Environmental Chemicals in Korea

South Korea University hospital based

2011-2013 352 352

COCOA

COhort for Childhood Origin of Asthma and allergic diseases15

Korea Hospital & Community based

2007-2016 2,400 2,400

EDC study

Environment and Development of Children Study

Seoul Community based 2008-2014 698 698

MOCEH

The Mothers and Children’s Environmental Health study16

Seoul, Ulsan and Cheonan

Hospital & Community based

2006-2010 1,751 1,751

PSKC Panel Study on Korean Children Korea Hospital based 2008 2,150 2,150

MALAYSIA USM Pregnancy Cohort

Universiti Sains Malaysia Pregnancy Cohort Study17

Kubang Kerian Hospital based 2010-2011 159 188

NEPAL Nepali Nepali Birth Cohort Study in Chitwan Valley18

Chitwan Hospital & Community based

2008Sep-Oct 100 100

PHILIPPINES CLHNS Cebu Longitudinal Health and Nutrition Survey19

Metro Cebu, Philippines

Community based 1983-1984 3,080 3,327

SINGAPORE GUSTO Growing Up in Singapore Towards healthy Outcomes20

Singapore Hospital based 2009-2010 1,190 1,247




SRI LANKA KCHS Kalutara Children’s Health Study Kalutara, Sri Lanka Community based 2014-2015 450 450

TAIWAN TBPS Taiwan Birth Panel Study21 Taipei and New Taipei Hospital based & Community based

2004-2005 486 486

TEC Taiwan Early-Life Cohort22

Hsinjuang, Jiayi, Yulin, Tainan, Kaohsiung, Taitung

Hospital based 2001-2005 1,589 1,589

TMICS

Taiwan Maternal and Infant Cohort Study23

Taipei, Hsinchu, Taichung, Changjua, Kaohsiung, Hualien

Hospital based 2000-2014 1,616 2,577

VIETNAM BienHoa study

BienHoa Dioxin Cohort study24 Bien Hoa Hospital based 2012Sep -Nov

200 200

DaDoCiV

Dioxin and Development of Children in Vietnam

Hanoi, Phu, Cat, Bien Hoa

Community based 2008-2013 200 200

DaNang study

DaNang Dioxin Cohort study DaNang Hospital based 2008-2010 241 241

*Targeted recruitment numbers.

Table 2. Collected biological samples of participating studies in Birth Cohort Consortium of Asia Child

Father Mother

1~6 6~24 2~5 5~10 ≧10

Cohort T1 T2 T3 at deliverya postpartum Birth mo mo year year year

LWBC

U B, P BM CB,M

U U U

NJMUBC B, Db, U, Se

B, Db, U B, Db,

U B, Db, U

A, B, Db, P, U, UC

BM CB, M BM U B, Db, U B, Db, U B, Db,

U

SBC B, Db,

Ds, U, Se B, Db, U

B, Db, U

B, Db, U P H CB

N, U B, U, N B, U, N B, U,

N HBC Study

B P

CB

Ds

Hokkaido B

B Db

CB, Db

U

Sapporob

B, Db P BM, H CB, Db

TSCD

B P, H BM CB

CHECK

B B, P BM CB, M, UC, U U U, H U, H

COCOA B, Db

B, Db U,

S B, Db, P

CB, Db S B

U, B, Db, S

U, B, Db,S U,B

EDC study

B,U

B,U

MOCEH

B, Db, H, U

B, Db, U P BM CB, Db U B, Db, U B, Db, U B, Db, U

PSKC

Child

Father Mother

1~6 6~24 2~5 5~10 ≧10

Cohort T1 T2 T3 at deliverya postpartum Birth mo mo year year year

USM Pregnancy Cohort

B B H BM

H

Nepali

UC, H

CB, M

B, H

CLHNS

B

GUSTO Ds

B, H, Db, Ds

B, P, Db BM, H, Ds UC, CB, Ds,

M Ds, S Ds, S Ds, H, S

KCHS

TBPS

B, Db, P, U

CB, Db

B, H, S, U U

B, Db, Ds, U

TEC

B, Db, P, U

CB, Db

B, Db, Ds, U

TMICS

B, Db, U U B, Db, U P BM CB, M

B, Db, U B, Db, U B, Db, H, U B, Db, H, U

BienHoa

BM CB, UC

U U

DaDoCiV

B BM

Ds Ds

DaNang

BM

U

Abbreviations: A, amniotic fluid; B, blood; BM, breast milk; CB, cord blood; Db, DNA from blood; Ds, DNA from buccal swab ; H, hair; M, meconium; P, placenta; T1, T2, T3, first, second and third trimesters; S, stool; Se, semen; N, nail; U, urine; UC, umbilical cord;

a biosample collected during the hospital stay for delivery b Sapporo cohort did its first enrollment during T2-T3

Table 3. Measurements of selected environmental exposure/biomarkera of participating studies in Birth Cohort Consortium of Asia

Cohort Indoor air pollutions

Outdoor air pollutions

Tobacco smoking

Metals and Metalloids

Pesticides Persistent organic

pollutants other chemical exposures

LWBC

18 metalse Organophosphate,

pyrethrin PBDEs

BPA, trclosan, nonylphenol

NJMUBCb PM2.5, PM10 Cotinine, Nicotine,

Myosmine 25 metalsf

Organophosphate pesticides, Carbamate

pesticides, Organochlorine insecticides, Pyrethroid pesticides, Herbicides,

Fungicides

PCBs, PFASs, PBDEs

Pheonls, VOCs, Phthalates,

Phytoestrogens, Polycyclic aromatic

hydrocarbons

SBC dust

18 metalse Organophosphate,

pyrethrin PCBs, PFASs, PBDEs

BPA, trclosan, nonylphenol

HBC Study

Cu, Se, Zn

Hokkaido dust mite, phthalate

Cotininec

Dioxin, PCBs, PFASs BPA, phthalates

Sapporo

Cotinine Hg DDT, Drins, Chlordane, Heptaclor, HCH, Mirex,

Toxaphene, HCB Dioxin, PCBs, PFASs BPA, phthalates

TSCD

MeHg, Pb, Cd

Organochlorine Dioxin, PCBs



Tobacco smoking




CHECK

Pb, Cd DDTs, chlordanes, HCB, HCHs, heptachlor, heptachlor epoxide, mirex

PBDEs, PCBs BPA, phthalates,

parabens

COCOA PM, Dust,

mite, Endotoxin

PM, NO2, O3 Cotinine

Phthalates

EDC study PM10, NO2, CO, O3, SO2

Cotinine Pb, Cd,

Hg PFASs

BPA, phthalates, Pyrethrids (3-PBA),

PAHs, VOCs

MOCEH

PM2.5, PM10, NO2,

HCHO, VOCs, dust

mite, endotoxin

PM2.5, PM10, NO2, HCHO,

VOCs Cotinineb

Pb, Cd, Hg, Mn

BPA, phthalates, PAHs

PSKC Humidity/

Mold Nicotine



Tobacco smoking





Nicotinec

Nepali

Pb, As, Cd, Zn, Se, Cu

CLHNS

GUSTO

PM2.5 Cotinine

KCHS PM2.5, Black

carbon PM2.5, Black

carbon

TBPS

Cotinine 18 metalse chlorpyrifos, cypermethrin, Flucythrinate

PFASs BPA, phenols, phthalates

TEC

PM2.5, PM10, NO2, HCHO,

VOCs

18 metalse

PFASs

TMICS

Cotinined 18 metalse DDT, Chlordane,

Heptaclor, HCH, HCB PFASs, PCBs, Dioxin BPA, phthalates, phenols

BienHoa

Dioxin



Tobacco smoking




DaDoCiV

Dioxin

DaNang Dioxin Abbreviations: BPA, bisphenol A; DDTs, dichlorodiphenyl trichloroethanes; HCB, hexachlorobenzene; HCHs, hexachlorocyclohexanes; PAHs, Polycyclic aromatic hydrocarbons; PBDEs, polybrominated diphenyl ester; PCB, polychlorinated biphenyl; PFASs, polyfluoroalkyl substances; VOCs,Volatile Organic Compounds; a prenatal exposure, unless specified b planned to measure in the future c measurement include prenatal and postal exposure d postnatal exposure e 18 metals: lead, arsenic, mercury, cadmium, beryllium, antimony, barium, cerium, platinum, thorium, manganese, zinc, copper, selenium, cobalt, molybdenum, gallium, uranium f 25 metals: Aluminum, Antimony, Arsenic, Barium, Beryllium, Cadmium, Calcium, Cesium, Chromium, Cobalt, Copper, Gallium, Iron, Lead, Manganese, Mercury, Molybdenum, Nickel, Platinum, Selenium, Thallium, Thorium, Tungsten, Uranium, Zinc

Table 4. Health outcomes/surrogate marker recorded by participating studies in Birth Cohort Consortium of Asia

Cohort Fetal growth Pregnancy outcome

Growth and obesity

Allergic disease and immune function

Neurodevelopment and behavior problems

Endocrine function and puberty

Others

LWBC BW, BL preterm birth,

congenital malformation

weight, height

GESELL, CBCL, IQ (WPPSI/ WISC), ADHD-RS

thyroid function, sex

hormone

NJMUBC fetal sonography,

BW, BL

preterm birth, congenital

malformation, miscarriage,

stillbirth, FGR, LBW, SGA, macrosomia

weight, height, head, fat

ISAAC

CBCL, DDST, GESELL,PSQ, Japan S-M social adaptability scale,

SPM, C-WISC


hormone

SBC fetal sonography,

BW, BL


malformation, stillbirth

weight, height, fat

ISAAC ASQ, ASQ-SE, Bayley III,

CBCL, M-CHAT, IQ (WPPSI/ WISC)


hormone


Growth and obesity




Others

HBC Study

fetal sonography, BW, BL

preterm birth, stillbirth, LBW,

SGA

weight, height, head

circumference ISAAC

ADHD-RS, ADI-R, ADOS, BISQ, Early Child

Behavioural Questionnaire, Edinburgh Handedness

Inventory, MacArthur CDI, M-CHAT, MSEL, SDQ,

WPPSI, VABS II

Eye gaze patterns for measuring

sociability and attention using Gazefinder®

Hokkaido BW, BL


malformation, miscarriage,

stillbirth, FGR, LBW, SGA

weight, height ISAAC

SCQ, SDQ, DCDQ,

M-CHAT, KIDS, CBCL, ADHD-Rating scale, Conners3P, WISC-IV

thyroid function,

reproductive hormones

Sapporo BW, BL


malformation, stillbirth, FGR,

LBW, SGA

weight, height IgE, ISAAC Bayley II, CBCL, WISC-III,

K-ABC, FT-II, DDST, WCST-KFS

thyroid function,

reproductive hormones


Growth and obesity




Others

TSCD BW, BL preterm birth,


weight, height

ADHD-Rating scale, Bayley II, Boston Naming Test, CBCL, CPT, Fagan Test, K-ABC, KSPD, NABS,

WISC-III, WISC_IV

thyroid function

CHECK BW, BL preterm birth weight, height

Bayley II, SMS tests

thyroid function

COCOA BW, BL preterm birth weight, height IgE,

IL-13, IFNγ, SPT, ISAAC

Mullen, ASQ, CBCL, IQ (WPPSI/ WISC), MSEL

EDC study fetal sonography,

BW


malformation weight, height

thyroid function,

sexual maturation, bone age

liver and kidney function

MOCEH fetal sonography,

BW, BL


malformation FGR, LBW, SGA

weight, height, head

ISAAC Bayley II, ASQ, WPPSI, CAT, CBCL, SRS,

SDQ, ARS


Growth and obesity




Others

PSKC BW, BL preterm birth weight, height,

head, waist ISAAC

ASQ(Korea), Bayley II, CBCL 1.5-5(Korea), DDST II(Korea), WPPSI(Korea),

REVT


BW, BL

weight, height, head, fat

(skinfold)

ASQ

Nepali BW, BL preterm birth

Brazelton III, Bayley II

CLHNS BW, BL preterm birth weight, height

GUSTO fetal sonography,

BW, BL


malformation FGR, LBW, SGA

weight, height, head, mid-arm,

abdominal circumference,

body composition

ISAAC, SPT ASQ, ASQ-SE, Bayley III,

CBCL, CTS, ITSEA, PEDS, PEDS:DM, Q-CHAT

KCHS BW, BL preterm birth,


weight, height

Respiratory Conditions

(Symptom diaries,

Pneumonia, etc.)


Growth and obesity




Others

TBPS fetal sonography,

BW, BL preterm birth, LBW, SGA

weight, height IgE, ISAAC CBCL, CDIIT, WPPSI

thyroid function,


TEC BW, BL preterm birth weight, height IgE, ISAAC CBCL sexual

maturation, bone age

TMICS BW, BL, fetal sonography


malformation weight, height IgE, ISAAC Bayley II, CBCL, WPPSI

thyroid function,


BienHoa BW, BL

weight, height, head and

abdominal circumference

Bayley III


hormone, gender

difference of behavior


Growth and obesity




Others

DaDoCiV BW, BL preterm birth

sex hormone

DaNang BW, BL

weight, height, head and

abdominal circumference

Bayley III, Kauffman-ABC II, M-ABC2, Eating

behavior, Autism Spectrum rating Scale (ASRS), ADHD

gender difference of

behavior

Abbreviations: ADHD rating scales, attention deficit hyperactivity disorder rating scales; ADI-R, Autism Diagnositc Interview Revised; ADOS, autism diagnositic observation scale; Bayley, Bayley Scales of Infant and Toddler Development; ASQ, Ages and Stages Questionnaire; ASQ-SE, ASQ- social Emotional; BIA, bioelectrical Impedance Analysis; BISQ, Brief Infant Sleep Questionnaire; BL, birth length; BW, birth weight; CBCL, Child Behavior Checklist; CPT, The Conners' Continuous Performance Test; Conners3P, The Conners Third edition Parent; CTS, Carey temperament Scales; DCDQ, developmental coordination disorder questionnaire; DDST, the Denver Developmental Screening Test; FGR, fetal growth restriction; FTII, The Fagan Test of Infant Intelligence; GA, gestational age; GESELL, the Gesell developmental schedules; IFNγ, Interferon gamma; IgE, immunoglobulin E; IL-13, Interleukin 13; ISAAC, The International Study of Asthma and Allergies in Childhood questionnaires; ITSEA, Infant Toddler Social Emotional Assessment; K-ABC, The Kaufman-Assessment Battery for Children; KIDS, Kinder Infant Development Scale; KSPD, Kyoto Scale of Psychological Development; LBW, low birth weight; MacArthur CDI, MacArthur Communicative Developmental Inventory; M-CHAT, modified checklist for autism in Toddlers; MSEL, Mullen Scales of Early learning; NABS, the Neonatal Behaviroal Assessment Scale; PEDS, Parent's Evaluation of Developmental Status; PEDS:DM, PEDS: Developmental Milestones; PST, Parent Symptom Questionnaire; Q-CHAT, Quantitative Checklist for Autism in Toddlers; REVT, Receptive and Expressive Vocabulary Test; SCID, structured clinical interview for DSM-IV Axis-I disorders; SCQ, social communication questionnaire; SDQ, the strength and difficulties questionnaire; SGA, small for gestational age; SPT, skin prick test; SMS, Social Maturity Scale; VABS II, Vineland Adaptive

Behavior Scales, second edition; WCST-KFS, Wisconsin Card Sorting Test-Keio-F–S version; WISC-III The Wechsler Intelligence Scale for Children-Third edition; WISC-IV The Wechsler Intelligence Scale for Children-Forth edition; WPPSI, Wechsler Preschool and Primary Scale of Intelligence.

Table 5-1 Maternal characteristics

Birth cohort

Age (years), N (mean±SD)

Pre-pregnancy BMI (kg/m2), N (mean±SD)

Educational level (years) N (%)

LWBC 756 (28.3±4.5) 741 (21.7±3.0) no education

5 (0.6) elementary

46 (6.0)

junior high school

303 (39.6)

senior high school

216 (28.2)

university 189 (24.7)

graduate school 7 (0.9)

NJMUBC * * * * * * * * * Shanghai * * * * * * * * *

HBC Study

1138 (31.4 ± 5.1)a

1138 (21.0 ± 3.4)

<12y (junior high

school) 68 (6.0)

12y (high

school) 317 (27.9)

13-15y (vocational &

college) 469 (41.2)

16y (university) 235 (20.7)

>16y (graduate school)

49 (4.3)

Hokkaido 18598

(29.8 ± 4.8) 19307

(21.2 ± 3.3)

≤9y (junior high

school) 1084 (5.5)

10-15y (high school or junior

college) 16553 (83.3)


Sapporo 510 (30.4 ± 4.9) 506 (21.2 ± 3.2)

≤12y 225 (44.3)

13-16y 274 (53.9)

≥17y 9 (1.8)

Birth cohort




TSCD 1348 (30.3 ± 4.8) 1348 (21.2 ± 3.1)

≤12y 756 (56.1)

>12y 591 (43.9)

CHECK 352 (33.5 ± 4.1) 352 (21.4 ± 3.3) N.A.

COCOA 2224 (32.6 ± 3.6) 2041 (20.7 ± 2.6) no education

1 (0.1) elementary

1 (0.1)

middle school

1 (0.1)

high school 110 (5.5)

vocational college 61 (3.0)


graduate school

430 (21.3)

EDC study 663 (31.4 ± 3.6) 662 (21.9 ± 3.0)

Junior high school 1 (0.15)

Senior high school 124 (18.7)


Graduate school

77 (11.6)

MOCEH 1604

(30.30 ± 3.74) 1432 (22.9 ± 3.0)

<12y (high school)

397 (26.5)

>12y (university or graduated

school) 1103 (73.5)

PSKC 2077

(31.3 ± 3.7) 2067 (21.2±2.9)

below high school

14 (0.6)


college 590 (28.6)


postgraduate degree

115 (5.6) USM Pregnancy Cohort

153 (28.9 ± 4.5) 153 (22.6 ± 4.1) no education

0 primary 1(0.7)

secondary 51(33.3)

diploma 64 (41.8)

university degree

26 (17.0)

postgraduate degree

11 (7.2)

Birth cohort




Nepali 100 (22.9 ± 3.7) 100 (23.2 ± 2.9) Mean year (SD)

9.2 (3.8) and Range 0 to 17

CLHNS 3327 (26.0 ± 6.0) N.A. no education

61 (1.8) elementary 1984 (59.6)


college 422 (12.7)

GUSTO 1247 (30.6 ± 5.2) 1141 (23.7 ± 4.8) no education

2 (0.2) primary 55

(4.5) secondary 451 (36.7)

GCE A Level/Diploma

311 (25.3)


KCHS 375 (29.6 ± 5.2) 326 (22.6 ± 4.2) Never gone to school 2 (0.5)

Primary education

3 (0.8)

Secondary education 96 (25.5)

G.C.E O/L 108 (28.6)

G.C.E O/L & Vocational education

2 (0.5)

G.C.E A/L 134 (35.5)

Degree 28 (7.4)

TBPS 486 (30.9 ± 4.6) 486 (20.9 ± 3.0) no education

3 (0.6) elementary

10 (2.1)

junior high school

32 (6.6)

senior high school

113 (23.3)

specialized training school

112 (23.1)


graduate school

66 (13.6)

Birth cohort




TEC 1521 (27.6 ± 8.9) 1265 (20.9 ± 3.3) no education

15 (1) elementary

50 (3.2)

junior high school

215 (13.8)

senior high school

823 (52.8)


303 (19.4)


graduate school

22 (1.4)

TMICS 1987 (31.1 ± 4.8) 1611 (21.9 ± 3.8) no education

5 (0.2) elementary

28 (1.4)

junior high school

203 (10.1)

senior high school

404 (20.1)

specialized training school/university

1141 (56.8)

graduate school

229 (11.4)

BienHoa 210 (28.6 ± 4.6) N.A. no education

1 (0.5) elementary

13(6.2)

junior high school

57 (27.1)

senior high school

70 (33.3)


39 (18.6)


DaDoCiV 236 (24.8 ± 3.6) N.A. elementary 34 (14.8)

junior high school

99 (43.2)

high school

67 (29.3)


DaNang 217 (27.8 ± 6.0) N.A. no education

9 (4.1) elementary 44 (20.3)

junior high school

94 (43.3)

senior high school

48 (22.1)


19 (8.8)

university 3 (1.4)

Abbreviations: N.A., not available.

a number of participating parturient [1138)] is not equal to participating infants [1258] because of multiple births and multiple entry of some of the parturient *: still recruiting subjects

Table 5-2 Maternal characteristics

Birth cohort Parity Smoking status during pregnancy,

N (%)

Environmental tobacco smoke Exposure during

pregnancy, N(%)

Alcohol drinking during pregnancy,

N(%) primipara multipara non-smoker Quitter smoker yes yes LWBC 489 (67.3) 238 (32.7) 766 (99.0) 4 (0.5) 4 (0.5) 339 (43.8) 0 NJMUBC * * * * * * * Shanghai * * * * * * * HBC Study 615 (54.0) 523 (46.0) 884 (77.6) 192 (16.8)a 62 (5.5)b N.A. 145 (12.8)c Hokkaido 8183 (43.8) 10498 (56.1) 9113 (54.5) 5284 (31.6) 2298 (13.7) 8662 (43.9)d 2438 (18.3) Sapporo 240 (47.7) 263 (52.3) 228 (44.9) 177 (34.8) 103 (20.3) 372 (73.2)e 157 (30.9) TSCD 725 (53.8) 623 (46.2) 1046(77.8) 222(16.5) 76(5.7) N.A. 140 (10.4) CHECK 136 (38.6) 216 (61.4) 252 (96.6)

9 (3.4) 131 (45.3) 37 (13.7)

COCOA 1099 (58.4) 782 (41.6) 164 (95.9)

7 (4.1) 1050 (60.6) 164 (9.4) EDC study 374 (56.6) 287 (43.4) 531 (92.7) 42 (7.3) 163 (24.6) 173 (30.6) MOCEH 681 (50.9) 657 (49.1) 1327 (88.2) 164 (10.9) 13 (0.9) 921 (65.1)d 75 (5.4) PSKC 984 (47.5) 1088 (52.5) 1813 (97.3) 33 (1.8) 17 (0.9) 556 (60.3) 502 (26.9) USM Pregnancy Cohort

43 (28.1) 110 (71.9) 153 (100.0) 0 0 65 (44.2) 0

Nepali 66 (66.0) 34 (34.0)

5 (5.0)

4 (4.0) CLHNS 768 (23.1) 2559 (76.9) 2875 (86.4)

452 (13.6)

813 (24.4)

GUSTO 545 (45.9) 643 (54.1) 1149 (97.5)

29 (2.5) 432 (38.0) 21 (1.8)

Birth cohort Parity Smoking status during pregnancy,

N (%)

Environmental tobacco smoke Exposure during

pregnancy, N(%)

Alcohol drinking during pregnancy,

N(%) primipara multipara non-smoker Quitter smoker yes yes KCHS 105 (29.6) 227 (63.9) 377 (100)

0 5 (1.3) 0

TBPS 230 (47.3) 256 (52.7) 460 (94.7)

26 (5.30) 139 (28.6) 21 (4.3) TEC 647 (47.2) 723 (52.8) 1452 (95.8) 16 (1.1) 48 (3.2) 618 (44.0) 32 (2.1) TMICS 682 (43.5) 887 (56.5) 1974 (98.4) 12 (0.6) 20 (1.0) N.A. 95 (4.7) BienHoa 77 (36.7) 133 (63.3) 207 (98.6) 0 3 (1.4) 136 (64.8) 10 (4.8) DaDoCiV 152 (64.7) 83 (35.3) 233 (99.2) 0 2 (0.8) 114 (48.5) 7 (3.0) DaNang 73 (30.5) 163 (69.5) 241 (100) 0 0 198 (82.8) 17 (8.7) a quitted before 12wks of gestational age b any smoking at/after 12wks of gestational age (including those quitted after 12 wks) c any drinking at/after 12wks of gestational age d assessed based on exposure of pregnant women at home or workplace e pregnant women who lived with smoker(s) during pregnancy *: still recruiting subjects

Table 6. Infant Characteristics

Birth cohort Male, N (%) Gestational age (weeks), N (mean±SD)

Birth weight (gm), N (mean±SD)

Preterm, N (%) Low birth weight, N (%)

LWBC 391 (51.2) 756 (39.3 ± 2.0) 763 (3419 ± 501) 30 (4.0) 17 (2.2) NJMUBC * * * * * SBC * * * * * HBC Study 648 (51.5) 1258 (38.9 ± 1.6) 1258 (2943 ± 435) 82 (6.5) 160 (12.7) Hokkaidoa 9204 (50.4) 18004 (38.7 ± 1.5) 18004 (3029 ± 420) 980 (5.4) 1592 (8.8) Sapporo 242 (48.0) 504 (38.9 ± 1.5) 504 (3039 ± 521) 35 (6.9) 34 (6.7) TSCD 696 (51.6) 1349 (39.6 ± 1.2) 1349 (3111 ± 355) N.A. N.A. CHECK 177 (50.4) 352 (39.3 ± 1.2) 352 (3256 ± 388) 4 (1.1) 13 (3.7) COCOA 1029 (52.0) 1915 (39.1 ± 1.3) 1690 (3184 ± 438) 80 (4.2) 94 (5.6) EDC study 356 (53.7) 660 (38.7 ± 1.5) 608 (3240 ± 447) 8 (1.2) 26 (4.3) MOCEH 774 (52.2) 1502 (39.0 ± 1.7) 1475 (3261 ± 444) 80 (5.3) 41 (2.8) PSKC 1059 (51.0) 2066 (39.2 ± 1.2) 2073 (3258 ± 408) 63(3.0) 62(3.0) USM Pregnancy Cohort

69 (45.1) 153 (38.8 ± 1.2) 153 (3110 ± 420) 0 11 (7.2)

Nepali 47 (47.0) 100 (38.9 ± 1.4) 100 (3029 ± 438)

CLHNS 1632 (53.0) 3052 (38.9 ± 2.6) 3040 (2987 ± 442) 478 (15.6) 372 (14.1) GUSTO 627 (52.7) 1190 (38.6 ± 1.7) 1190 (3065 ± 484) 103 (8.7) 117 (9.8) KCHS 170 (48.0) 327 (38.3 ± 2.1) 354 (2940 ± 567) 38 (10.7) 43 (12.1) TBPS 247 (50.8) 486 (38.5 ± 1.7) 486 (3158 ± 476) 42 (8.6) 28 (5.8)

Birth cohort Male, N (%) Gestational age (weeks), N (mean±SD)

Birth weight (gm), N (mean±SD)

Preterm, N (%) Low birth weight, N (%)

TEC 809 (51.9) 1510 (38.9 ± 1.2) 1480 (3094 ± 429) 76 (5.0) 80 (5.4) TMICS 712 (53.5) 1332 (38.4 ± 2.0) 1308 (3087 ± 465) 90 (6.8) 90 (6.9) BienHoa 111 (52.9) 210 (38.9 ± 2.6) 210 (3297 ± 411) 6 (2.9) 2 (1.0) DaDoCiV 125 (53.2) N.A. 120 (3211 ± 433) N.A. 6 (5.0) DaNang 125 (57.4) 217 (39.5 ± 0.8) 217 (3228 ± 375) 0 4 (1.8) Abbreviations: N.A., not available. a In Hokkaido cohort, gestational age, birth weight, preterm birth and low birth weight were calculated among subjects with live birth *: still recruiting subjects

Birth Cohort Consortium of Asia : Current and Future Perspectives … · 2019. 10. 8. · Acquired...

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